Updated: 2020-07-28 07:23:55 PDT

Original version created 2020-05-03. See below for revision history

Intro


The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.

This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.


This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Maps

There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.

Cases and Deaths per Capita

This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.

Contained and Uncontained Disease

A key indicator of mitigation is capping infection. Uncontained disease growth threatens epidemic conditions.

This visualization shows places where current disease levels are below their peak levels (’contained“) and where current disease levels are at an all time high (”uncontained").

Reproduction and Control

\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from log_2(R_e) > 0 to log_2(R_e) < 0).

After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.

\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]

state R_e cases daily_cases
North Dakota 1.26 5932 140
Oklahoma 1.26 32223 1000
Mississippi 1.24 53552 1450
Missouri 1.24 39432 1143
Wyoming 1.24 2537 55
Montana 1.22 3462 140
Kentucky 1.20 28565 706
Tennessee 1.18 94260 2582
Arkansas 1.17 38300 859
Virginia 1.17 69102 929
New Mexico 1.16 19524 349
West Virginia 1.16 6143 152
Indiana 1.15 64707 916
Alabama 1.14 82528 2192
California 1.14 473985 11148
Colorado 1.14 44798 608
Connecticut 1.14 48668 116
Maryland 1.14 85076 875
Nebraska 1.14 24736 271
Nevada 1.14 44748 1288
Wisconsin 1.14 50112 1032
Idaho 1.13 19353 631
Illinois 1.13 173148 1444
Louisiana 1.13 110586 2489
Minnesota 1.13 51591 735
Florida 1.12 442325 13025
Georgia 1.12 155995 3676
New Jersey 1.12 180884 329
South Dakota 1.10 8259 67
Maine 1.09 3831 21
New Hampshire 1.09 6459 30
Ohio 1.09 86114 1505
Oregon 1.09 17287 371
Washington 1.09 55982 976
Kansas 1.08 26544 512
North Carolina 1.08 115794 2161
Pennsylvania 1.08 113033 957
Texas 1.07 414883 10560
Delaware 1.06 14224 108
Massachusetts 1.06 115544 301
Michigan 1.06 87420 747
South Carolina 1.06 84276 1957
Iowa 1.05 43124 560
Utah 1.05 38899 659
Vermont 1.05 1410 8
Arizona 1.02 166005 2941
New York 1.01 417283 723
Rhode Island 0.93 16746 65

National Statistics

Total & Active Cases, and Deaths

These trend charts show the national disease statistics. The raw data are shown. since these showdaily trends that are systematically related ot the M-F work week, possibly due to reporting delays, numbers showsn

## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 row(s) containing missing values (geom_path).

Mortality Trend

National \(R_e\)

There is also large variation in the distribution of \(R_e\) values. This shows how that distribution has changed over the last three weeks. As a reminder, for disease reduction, \(R_e\) needs to be sustained below 1.0.

Trend

Distribution of \(R_e\) Values

Regional Snapshots

Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.

How to read the charts

There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.

(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).


Washington and Oregon

WA
county ST case rank severity R_e cases cases/100k daily cases
Okanogan WA 13 1 1.5 680 1630 47
King WA 1 2 1.1 14643 680 198
Spokane WA 5 3 1.2 3464 700 107
Pierce WA 4 4 1.1 4926 570 100
Kitsap WA 16 5 1.4 535 200 23
Snohomish WA 3 6 1.1 5422 690 64
Douglas WA 14 7 1.2 675 1630 26
Yakima WA 2 9 0.9 10245 4110 103
Benton WA 6 10 1.0 3435 1770 62
Franklin WA 7 11 1.0 3237 3570 48
Grant WA 9 12 1.1 1174 1240 24
Clark WA 8 15 0.9 1722 370 32
OR
county ST case rank severity R_e cases cases/100k daily cases
Multnomah OR 1 1 1.1 4073 510 84
Umatilla OR 4 2 1.1 1775 2310 54
Washington OR 2 3 1.1 2568 440 50
Marion OR 3 4 1.1 2460 730 44
Deschutes OR 7 5 1.2 476 260 18
Jefferson OR 13 6 1.3 267 1150 10
Clackamas OR 5 7 1.0 1296 320 22
Lane OR 8 12 1.0 469 130 11
Malheur OR 6 15 0.8 606 1990 13
Union OR 9 23 0.5 388 1490 1
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Kern CA 6 1 1.8 17513 1980 1338
Los Angeles CA 1 2 1.0 178768 1770 3083
San Bernardino CA 4 3 1.2 30241 1420 867
Riverside CA 2 4 1.1 35849 1500 739
Fresno CA 7 5 1.2 13312 1360 411
San Diego CA 5 6 1.1 27877 840 567
Orange CA 3 7 1.0 35541 1120 749
San Joaquin CA 8 8 1.2 10872 1480 336
Alameda CA 9 17 1.1 10672 650 196

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
Maricopa AZ 1 1 1.0 111590 2620 2120
Pima AZ 2 2 1.0 15256 1500 238
Yuma AZ 3 3 1.1 10446 5030 147
Pinal AZ 4 4 1.0 7653 1820 136
Mohave AZ 8 5 1.0 2841 1380 70
Gila AZ 12 6 1.2 720 1350 23
Graham AZ 14 7 1.2 395 1040 16
Apache AZ 6 8 1.1 2942 4110 26
Santa Cruz AZ 9 10 1.1 2535 5440 26
Navajo AZ 5 11 0.9 5161 4750 49
Coconino AZ 7 12 0.9 2901 2070 28
CO
county ST case rank severity R_e cases cases/100k daily cases
Denver CO 1 1 1.2 9233 1330 108
El Paso CO 4 2 1.1 4276 620 92
Arapahoe CO 2 3 1.1 6568 1030 71
Adams CO 3 4 1.1 5617 1130 67
Jefferson CO 5 5 1.2 3675 640 52
Chaffee CO 17 6 1.3 292 1520 17
Douglas CO 8 7 1.1 1547 470 29
Larimer CO 9 8 1.1 1285 380 27
Weld CO 6 10 1.0 3449 1170 30
Boulder CO 7 12 1.1 1804 560 19
UT
county ST case rank severity R_e cases cases/100k daily cases
Salt Lake UT 1 1 1.0 18526 1650 261
Utah UT 2 2 1.1 7364 1250 150
Davis UT 3 3 1.1 2807 820 69
Washington UT 5 4 1.1 2216 1380 45
Weber UT 4 5 1.0 2418 980 57
Cache UT 6 6 1.1 1760 1440 11
San Juan UT 8 7 1.0 596 3900 9
Summit UT 7 11 0.9 679 1680 6
Wasatch UT 9 12 0.9 520 1700 4
NM
county ST case rank severity R_e cases cases/100k daily cases
Cibola NM 11 1 1.8 346 1280 12
Lincoln NM 20 2 1.8 79 410 8
Bernalillo NM 1 3 1.1 4550 670 110
Lea NM 7 4 1.3 553 790 26
Doña Ana NM 4 5 1.1 2071 960 46
Rio Arriba NM 12 6 1.2 301 770 14
Otero NM 5 7 1.3 1051 1600 10
McKinley NM 2 8 1.0 3925 5390 21
Curry NM 9 9 1.2 386 770 11
Santa Fe NM 8 12 1.1 517 350 13
Sandoval NM 6 13 1.0 1023 730 14
San Juan NM 3 17 0.8 2942 2310 13

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Middlesex NJ 4 1 1.2 17761 2150 42
Ocean NJ 7 2 1.2 10278 1740 31
Camden NJ 9 3 1.1 8134 1600 31
Burlington NJ 12 4 1.2 5641 1260 19
Bergen NJ 1 5 1.1 20511 2210 28
Essex NJ 2 6 1.1 19552 2460 21
Gloucester NJ 16 7 1.1 2979 1020 18
Monmouth NJ 8 8 1.0 9980 1600 28
Passaic NJ 5 10 1.1 17462 3460 18
Hudson NJ 3 15 0.9 19551 2920 14
Union NJ 6 16 0.9 16749 3030 8
PA
county ST case rank severity R_e cases cases/100k daily cases
Philadelphia PA 1 1 1.1 29640 1880 157
Allegheny PA 4 2 1.0 7625 620 184
Delaware PA 3 3 1.1 8337 1480 59
Chester PA 9 4 1.1 4688 910 48
Montgomery PA 2 5 1.1 9528 1160 50
Bucks PA 5 6 1.1 6731 1070 47
Armstrong PA 40 7 1.4 164 250 8
Berks PA 7 8 1.1 5037 1210 27
Lancaster PA 6 10 1.0 5335 990 36
Lehigh PA 8 16 1.0 4698 1300 19
MD
county ST case rank severity R_e cases cases/100k daily cases
Baltimore MD 3 1 1.2 11070 1340 183
Baltimore city MD 4 2 1.1 10514 1710 153
Prince George’s MD 1 3 1.1 22136 2440 144
Montgomery MD 2 4 1.1 17143 1650 99
Anne Arundel MD 5 5 1.1 6456 1140 69
Harford MD 9 6 1.2 1623 650 28
Worcester MD 16 7 1.3 513 990 17
Howard MD 6 8 1.1 3392 1080 40
Frederick MD 7 10 1.1 2911 1170 24
Charles MD 8 13 1.1 1749 1110 18
VA
county ST case rank severity R_e cases cases/100k daily cases
Virginia Beach city VA 5 1 1.3 3727 830 171
Patrick VA 63 2 1.8 98 550 7
Lee VA 67 3 1.8 81 340 8
Norfolk city VA 7 4 1.2 2906 1180 110
Newport News city VA 9 5 1.2 1535 850 63
Fairfax VA 1 6 1.1 15402 1350 67
Suffolk city VA 13 7 1.2 954 1070 33
Henrico VA 6 8 1.2 3320 1020 38
Prince William VA 2 10 1.1 8531 1870 51
Chesterfield VA 4 11 1.1 3773 1110 41
Loudoun VA 3 13 1.0 4882 1270 31
Arlington VA 8 27 0.9 2830 1220 13
WV
county ST case rank severity R_e cases cases/100k daily cases
Logan WV 22 1 1.7 85 250 6
Kanawha WV 2 2 1.3 712 380 26
Mingo WV 18 3 1.5 106 430 8
Cabell WV 5 4 1.2 285 300 8
Ohio WV 6 5 1.1 239 560 8
Putnam WV 13 6 1.2 150 260 6
Raleigh WV 14 7 1.2 140 180 5
Berkeley WV 3 10 1.0 620 550 6
Monongalia WV 1 11 0.8 892 850 20
Wayne WV 9 13 1.0 183 450 4
Jefferson WV 4 19 0.8 286 510 2
Wood WV 7 20 0.6 231 270 2
Randolph WV 8 22 0.6 209 720 1
DE
county ST case rank severity R_e cases cases/100k daily cases
New Castle DE 1 1 1.1 6536 1180 56
Sussex DE 2 2 1.0 5548 2530 31
Kent DE 3 3 1.0 2140 1220 20

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Baldwin AL 7 1 1.3 2890 1390 144
Jefferson AL 1 2 1.1 10842 1640 295
Mobile AL 2 3 1.2 7483 1800 199
Calhoun AL 16 4 1.3 1182 1030 60
Madison AL 4 5 1.1 4629 1290 165
Houston AL 17 6 1.3 1179 1130 49
Montgomery AL 3 7 1.1 5853 2580 96
Shelby AL 6 8 1.1 2896 1370 88
Tuscaloosa AL 5 12 1.1 3676 1780 71
Marshall AL 8 16 1.1 2778 2920 55
Lee AL 9 19 1.0 2459 1540 54
MS
county ST case rank severity R_e cases cases/100k daily cases
Jackson MS 6 1 1.4 1664 1170 88
Hinds MS 1 2 1.3 4633 1920 141
Alcorn MS 54 3 1.6 285 770 21
Oktibbeha MS 15 4 1.5 876 1770 28
Coahoma MS 35 5 1.4 581 2440 35
Rankin MS 4 6 1.2 1883 1250 64
Harrison MS 5 7 1.2 1850 910 58
Madison MS 3 9 1.2 2098 2030 51
Washington MS 9 11 1.2 1338 2840 49
DeSoto MS 2 12 1.1 2885 1640 69
Jones MS 7 16 1.2 1579 2310 30
Forrest MS 8 17 1.2 1431 1890 33
LA
county ST case rank severity R_e cases cases/100k daily cases
Allen LA 26 1 1.4 1029 4010 56
Calcasieu LA 6 2 1.1 5691 2840 194
East Baton Rouge LA 3 3 1.1 9963 2240 216
Jefferson LA 1 4 1.1 13656 3140 182
Vernon LA 36 5 1.4 619 1210 33
Vermilion LA 23 6 1.3 1149 1920 52
St. Landry LA 17 7 1.2 1963 2350 71
Lafayette LA 4 8 1.0 6000 2500 150
Caddo LA 5 9 1.1 5753 2320 106
Tangipahoa LA 9 10 1.1 2896 2220 77
St. Tammany LA 7 12 1.1 4388 1740 92
Ouachita LA 8 14 1.1 4146 2660 77
Orleans LA 2 20 1.0 10011 2570 95

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Miami-Dade FL 1 1 1.2 109284 4020 3530
Columbia FL 30 2 1.6 2444 3540 200
Broward FL 2 3 1.1 51996 2720 1726
Palm Beach FL 3 4 1.1 31372 2170 781
Wakulla FL 51 5 1.6 559 1750 47
Bay FL 25 6 1.4 3112 1710 174
Marion FL 22 7 1.3 3679 1060 190
Orange FL 4 8 1.0 28087 2130 686
Duval FL 6 11 1.0 20570 2230 531
Hillsborough FL 5 12 1.0 28008 2030 587
Polk FL 9 13 1.1 11972 1790 342
Lee FL 8 19 1.0 15130 2110 343
Pinellas FL 7 20 1.0 15687 1640 302
GA
county ST case rank severity R_e cases cases/100k daily cases
Fulton GA 1 1 1.1 16376 1600 438
Cobb GA 4 2 1.2 10374 1390 268
Gwinnett GA 2 3 1.1 15710 1740 311
Wayne GA 46 4 1.4 592 1990 42
Chatham GA 6 5 1.2 4472 1560 150
DeKalb GA 3 6 1.1 11412 1540 226
Richmond GA 9 7 1.2 2930 1450 101
Clayton GA 7 9 1.1 4117 1480 97
Hall GA 5 16 1.0 4997 2550 83
Muscogee GA 8 28 1.0 3967 2020 78

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Real TX 157 1 3.8 97 2860 23
Jasper TX 112 2 2.4 215 610 23
Madison TX 91 3 2.2 355 2510 36
Bexar TX 3 4 1.1 39155 2030 1414
Harris TX 1 5 1.1 67438 1470 1572
Hidalgo TX 6 6 1.1 16632 1960 665
Cameron TX 9 7 1.2 8538 2020 386
Tarrant TX 4 8 1.1 26232 1300 592
Dallas TX 2 9 1.0 48340 1870 971
Nueces TX 8 18 1.0 11091 3080 314
El Paso TX 7 19 1.0 13682 1630 292
Travis TX 5 24 0.9 20177 1680 304
OK
county ST case rank severity R_e cases cases/100k daily cases
Hughes OK 45 1 3.2 76 560 14
Jackson OK 12 2 1.7 417 1640 41
Tulsa OK 2 3 1.2 7777 1210 210
Oklahoma OK 1 4 1.1 7992 1020 244
Garfield OK 24 5 1.5 276 440 18
Cleveland OK 3 6 1.2 2170 780 67
Sequoyah OK 34 7 1.6 154 370 10
Rogers OK 8 8 1.4 628 690 28
Canadian OK 5 14 1.1 870 640 29
Payne OK 9 24 1.2 612 750 11
McCurtain OK 6 26 1.1 791 2400 13
Comanche OK 7 27 1.1 688 560 14
Texas OK 4 43 1.0 1019 4820 1

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Jackson MI 7 1 1.4 2322 1460 17
Wayne MI 1 2 1.0 26242 1490 144
Macomb MI 3 3 1.1 9208 1060 82
Oakland MI 2 4 1.0 14091 1130 96
Kent MI 4 5 1.0 6847 1060 68
Genesee MI 5 6 1.1 3331 810 31
Saginaw MI 8 7 1.1 1758 910 27
Washtenaw MI 6 9 1.0 2788 760 25
Ottawa MI 9 13 1.0 1648 580 23
WI
county ST case rank severity R_e cases cases/100k daily cases
Waukesha WI 4 1 1.3 3095 780 126
Milwaukee WI 1 2 1.1 18511 1940 338
Racine WI 5 3 1.2 2960 1510 48
Waupaca WI 22 4 1.4 314 610 14
Barron WI 38 5 1.5 119 260 8
Kenosha WI 6 6 1.1 2309 1370 43
Marathon WI 15 7 1.2 504 370 19
Outagamie WI 9 8 1.1 983 530 26
Brown WI 3 10 1.0 3806 1470 40
Walworth WI 8 12 1.1 1078 1050 23
Dane WI 2 13 0.9 3934 740 56
Rock WI 7 18 1.0 1448 900 21

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
Hennepin MN 1 1 1.1 16471 1330 233
Ramsey MN 2 2 1.2 6312 1170 84
Dakota MN 3 3 1.1 3521 840 64
Anoka MN 4 4 1.2 3038 870 50
Sherburne MN 17 5 1.3 550 590 18
Beltrami MN 29 6 1.4 166 360 12
Washington MN 7 7 1.1 1701 670 33
Scott MN 9 8 1.1 1197 830 25
Olmsted MN 8 13 1.0 1538 1000 17
Stearns MN 5 20 0.9 2743 1750 13
Nobles MN 6 33 0.9 1730 7920 3
SD
county ST case rank severity R_e cases cases/100k daily cases
Minnehaha SD 1 1 1.1 4016 2150 21
Lincoln SD 4 2 1.3 477 870 8
Pennington SD 2 3 0.9 799 730 10
Davison SD 15 4 0.9 84 420 2
Beadle SD 3 5 0.9 584 3180 2
Union SD 6 6 0.8 178 1170 2
Yankton SD 11 7 0.9 100 440 1
Brown SD 5 8 0.8 379 980 2
Codington SD 7 10 0.7 114 410 1
Brookings SD 8 11 0.7 113 330 1
Buffalo SD 9 13 0.4 108 5260 1
ND
county ST case rank severity R_e cases cases/100k daily cases
Burleigh ND 2 1 1.3 787 840 32
Morton ND 4 2 1.3 232 760 10
Williams ND 5 3 1.2 226 660 13
Ward ND 6 4 1.3 154 220 8
Cass ND 1 5 1.0 2826 1620 25
Grand Forks ND 3 6 1.1 608 860 13
Stutsman ND 10 7 1.3 98 470 3
Stark ND 7 8 1.1 150 490 5
Walsh ND 9 9 0.9 101 940 3
Mountrail ND 8 10 0.8 108 1060 2

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
Fairfield CT 1 1 1.2 17433 1850 42
Hartford CT 3 2 1.2 12362 1380 36
New Haven CT 2 3 1.0 12936 1510 24
Litchfield CT 4 4 1.2 1561 850 5
Tolland CT 7 5 1.1 966 640 3
New London CT 5 6 0.8 1372 510 3
Windham CT 8 7 0.8 665 570 2
Middlesex CT 6 8 0.7 1372 840 2
MA
county ST case rank severity R_e cases cases/100k daily cases
Middlesex MA 1 1 1.1 25380 1590 63
Worcester MA 4 2 1.1 13116 1600 35
Norfolk MA 5 3 1.1 10006 1430 39
Suffolk MA 2 4 1.0 20917 2640 43
Essex MA 3 5 1.0 16991 2180 35
Bristol MA 7 6 1.0 8909 1590 29
Hampden MA 8 7 1.0 7300 1560 21
Plymouth MA 6 8 1.1 8993 1760 14
Barnstable MA 9 9 1.1 1698 790 10
RI
county ST case rank severity R_e cases cases/100k daily cases
Providence RI 1 1 0.9 14133 2230 54
Kent RI 2 2 0.8 1366 830 6
Washington RI 3 3 0.7 579 460 2
Bristol RI 5 4 0.7 296 610 2
Newport RI 4 5 0.4 371 450 1

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
New York City NY 1 1 1.0 228827 2710 344
Suffolk NY 3 2 1.0 42951 2890 60
Nassau NY 2 3 1.0 43007 3170 47
Erie NY 7 4 1.0 8347 910 40
Westchester NY 4 5 1.0 35804 3700 34
Monroe NY 8 6 1.0 4633 620 29
Albany NY 11 7 1.1 2463 800 18
Orange NY 6 11 0.9 11029 2920 11
Dutchess NY 9 14 0.9 4439 1510 9
Rockland NY 5 16 0.8 13854 4280 8

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Chittenden VT 1 1 0.9 719 440 4
Rutland VT 4 2 0.7 85 140 1
Bennington VT 5 3 0.6 83 230 1
Franklin VT 2 4 0.6 115 230 0
Windham VT 3 5 0.4 102 240 0
ME
county ST case rank severity R_e cases cases/100k daily cases
Androscoggin ME 3 1 1.2 535 500 3
Cumberland ME 1 2 0.9 2022 690 9
York ME 2 3 1.0 623 310 4
Kennebec ME 4 4 0.9 158 130 1
Penobscot ME 5 5 0.7 142 90 1
NH
county ST case rank severity R_e cases cases/100k daily cases
Hillsborough NH 1 1 1.1 3675 890 19
Rockingham NH 2 2 0.9 1586 520 4
Strafford NH 4 3 0.9 319 250 1
Merrimack NH 3 4 0.8 453 300 2
Carroll NH 8 5 0.8 82 170 1
Belknap NH 5 6 0.6 103 170 1
Grafton NH 6 7 0.5 102 110 0
Cheshire NH 7 8 0.4 82 110 0

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Florence SC 11 1 1.2 2562 1850 78
Richland SC 4 2 1.1 7293 1790 167
Hampton SC 43 3 1.5 279 1410 18
Beaufort SC 8 4 1.1 3097 1700 87
Lexington SC 5 5 1.1 4340 1520 103
Greenville SC 2 6 1.0 9656 1940 169
Cherokee SC 31 7 1.4 492 870 23
Charleston SC 1 8 0.9 11029 2790 208
York SC 9 10 1.1 2938 1140 78
Berkeley SC 6 13 1.0 3629 1740 94
Horry SC 3 16 0.9 7676 2390 117
Spartanburg SC 7 21 0.9 3570 1180 70
NC
county ST case rank severity R_e cases cases/100k daily cases
Mecklenburg NC 1 1 1.0 19708 1870 322
Wake NC 2 2 1.0 10331 990 202
Pitt NC 21 3 1.3 1513 850 50
Chowan NC 83 4 1.6 103 730 8
Cumberland NC 9 5 1.2 2380 720 62
Buncombe NC 22 6 1.2 1499 590 52
Guilford NC 4 7 1.1 4828 920 92
Gaston NC 6 11 1.1 2791 1290 73
Durham NC 3 15 1.0 5616 1830 70
Forsyth NC 5 17 1.0 4655 1250 66
Union NC 8 18 1.0 2573 1140 53
Johnston NC 7 23 1.0 2699 1410 43

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Big Horn MT 3 1 1.6 226 1690 13
Flathead MT 5 2 1.4 202 210 13
Gallatin MT 2 3 1.2 824 790 32
Cascade MT 7 4 1.4 118 140 7
Yellowstone MT 1 5 1.0 930 590 32
Missoula MT 4 6 1.1 216 190 7
Lake MT 6 7 1.0 154 520 8
Lewis and Clark MT 8 8 1.1 113 170 5
WY
county ST case rank severity R_e cases cases/100k daily cases
Teton WY 3 1 1.3 294 1270 13
Lincoln WY 9 2 1.3 92 480 5
Sweetwater WY 5 3 1.1 227 510 6
Laramie WY 2 4 1.0 426 440 6
Fremont WY 1 5 1.0 459 1150 5
Albany WY 10 6 1.0 83 220 3
Park WY 8 7 1.1 102 350 2
Natrona WY 6 8 0.9 197 240 2
Uinta WY 4 9 0.8 237 1150 2
Campbell WY 7 10 0.8 109 230 2
ID
county ST case rank severity R_e cases cases/100k daily cases
Canyon ID 2 1 1.2 4424 2080 183
Bonneville ID 6 2 1.5 550 490 34
Ada ID 1 3 1.1 7498 1680 231
Kootenai ID 3 4 1.1 1398 910 50
Jefferson ID 21 5 1.4 98 350 7
Minidoka ID 9 6 1.2 387 1880 10
Bannock ID 10 7 1.2 318 370 12
Twin Falls ID 4 8 1.1 1068 1280 18
Cassia ID 7 10 1.1 425 1800 8
Jerome ID 8 17 0.8 387 1650 5
Blaine ID 5 20 0.6 564 2560 1

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Hancock OH 44 1 1.8 237 310 19
Franklin OH 1 2 1.0 16112 1260 286
Lucas OH 4 3 1.2 4170 960 95
Cuyahoga OH 2 4 1.0 11976 960 189
Ross OH 43 5 1.5 274 360 13
Montgomery OH 5 6 1.1 3600 680 82
Licking OH 20 7 1.3 951 550 32
Hamilton OH 3 8 1.0 8684 1070 113
Summit OH 6 10 1.1 2951 540 45
Butler OH 8 13 1.1 2467 650 46
Marion OH 7 47 1.0 2829 4330 6
Pickaway OH 9 50 0.9 2319 4040 6
IL
county ST case rank severity R_e cases cases/100k daily cases
Cook IL 1 1 1.1 102922 1970 543
Madison IL 9 2 1.2 1882 710 57
Jackson IL 18 3 1.5 521 890 17
DuPage IL 3 4 1.1 10955 1180 95
Lake IL 2 5 1.1 11504 1630 86
St. Clair IL 6 6 1.1 3580 1360 80
Macon IL 24 7 1.5 349 330 11
Will IL 5 8 1.1 8156 1180 63
Kane IL 4 10 1.1 8815 1660 49
McHenry IL 8 15 1.1 2751 890 32
Winnebago IL 7 16 1.1 3532 1230 26
IN
county ST case rank severity R_e cases cases/100k daily cases
Marion IN 1 1 1.2 13906 1470 136
Vanderburgh IN 10 2 1.2 1523 840 60
Hamilton IN 6 3 1.2 2281 720 44
Lake IN 2 4 1.0 6804 1400 81
Tipton IN 75 5 1.6 80 530 5
Dubois IN 27 6 1.3 570 1340 22
St. Joseph IN 5 7 1.1 2864 1060 52
Elkhart IN 3 10 1.0 4500 2210 55
Allen IN 4 13 1.1 3398 920 32
Johnson IN 9 22 1.1 1590 1050 16
Hendricks IN 8 25 1.1 1679 1040 14
Cass IN 7 27 1.2 1719 4510 6

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
Knox TN 5 1 1.3 3371 740 153
Washington TN 22 2 1.5 730 570 50
Henderson TN 38 3 1.6 346 1240 30
Roane TN 53 4 1.7 242 460 19
Shelby TN 2 5 1.1 19270 2060 390
Unicoi TN 75 6 1.9 104 580 6
Davidson TN 1 7 1.0 20404 2980 407
Rutherford TN 3 11 1.1 5553 1810 148
Hamilton TN 4 13 1.1 5172 1450 131
Sumner TN 7 23 1.1 2996 1670 74
Williamson TN 6 29 1.0 3063 1400 84
Wilson TN 8 37 1.0 1928 1450 50
Trousdale TN 9 78 0.8 1567 16370 3
KY
county ST case rank severity R_e cases cases/100k daily cases
Jefferson KY 1 1 1.3 6243 810 150
Oldham KY 9 2 1.5 552 840 38
Harlan KY 33 3 1.6 179 660 16
Scott KY 24 4 1.5 262 490 15
Boyle KY 48 5 1.6 106 350 7
Fayette KY 2 6 1.1 2872 900 65
Barren KY 22 7 1.4 268 610 14
Warren KY 3 8 1.2 2253 1780 40
Kenton KY 4 9 1.2 1216 740 26
Boone KY 5 18 1.0 949 740 16
Daviess KY 7 20 1.1 651 650 11
Shelby KY 6 31 1.0 671 1430 7
Muhlenberg KY 8 44 0.8 623 2000 4

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
Nodaway MO 47 1 2.3 92 410 11
St. Louis MO 1 2 1.3 11213 1120 306
Camden MO 27 3 1.8 203 450 15
St. Charles MO 3 4 1.2 3199 820 123
St. Louis city MO 2 5 1.2 4056 1300 90
Jackson MO 4 6 1.2 2774 400 84
Greene MO 7 7 1.2 1111 390 49
Jefferson MO 5 10 1.2 1218 550 40
Jasper MO 6 15 1.2 1128 950 28
Boone MO 8 22 1.0 1087 620 25
Buchanan MO 9 36 1.0 1024 1150 6
AR
county ST case rank severity R_e cases cases/100k daily cases
Newton AR 52 1 4.0 98 1250 23
Independence AR 31 2 1.8 217 580 18
Garland AR 17 3 1.4 696 710 34
Pulaski AR 2 4 1.1 4500 1140 115
Craighead AR 12 5 1.3 990 940 38
Franklin AR 55 6 1.6 83 470 6
Washington AR 1 7 1.0 5768 2520 84
Sebastian AR 4 9 1.1 1469 1150 48
Benton AR 3 11 1.0 4284 1650 58
Pope AR 9 13 1.1 1095 1720 34
Jefferson AR 6 14 1.1 1203 1710 27
Crittenden AR 8 16 1.2 1105 2250 21
Lincoln AR 7 40 0.9 1160 8470 6
Hot Spring AR 5 53 0.4 1453 4330 9

Conclusions

It’s in control some places, but not all places. And many places are completely out-of-control.

Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK



Built with R Version 4.0.2
This document took 1256.4 seconds to compute.
2020-07-28 07:44:52

version history

Today is 2020-07-28.
69 days ago: Multiple states.
61 days ago: \(R_e\) computation.
58 days ago: created color coding for \(R_e\) plots.
53 days ago: Reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
53 days ago: “persistence” time evolution.
46 days ago: “In control” mapping.
46 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
38 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
33 days ago: Added Per Capita US Map.
31 days ago: Deprecated national map.
27 days ago: added state “Hot 10” analysis.
22 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
20 days ago: added per capita disease and mortaility to state-level analysis.
8 days ago: changed to county boundarieson national map for per capita disease.
3 days ago: corrected factor of two error in death trend data.

Appendix: Methods

Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov

Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.

\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis

Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]

\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.

Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…

Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation

\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]

Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.


DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.